Method of and apparatus for displaying and analyzing a physiological signal

ABSTRACT

A method of displaying a representation of a physiological signal produced by a patient. The method includes the acts of obtaining a portion of at least one physiological signal acquired from the patient, determining an area to display, and constructing a virtual image representing at least a portion of the patient. The virtual image including (M) polygonal areas. The method further includes transforming the obtained signal to a plurality of values, assigning each value to one of the (M) polygonal areas, assigning a visual characteristic to each polygonal area based in part on the assigned values, and displaying at least a portion of the virtual image including the assigned visual characteristics. The invention further provides a method of optimal feature selection for the classification of the physiological signals produced by a patient.

RELATED APPLICATION

This application is a continuation application of U.S. patentapplication Ser. No. 09/922,627, titled METHOD OF AND APPARATUS FORDISPLAYING AND ANALYZING A PHYSIOLOGICAL SIGNAL, filed Aug. 6, 2001, theentire content of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

The invention relates to a method of and apparatus for displaying andanalyzing a physiological signal of a patient, and particularly a methodof and apparatus for displaying and analyzing a physiological signalwhere the physiological signal is represented by a signal acquired atthe patient's body surface and the representative signal is transformedto include information not directly obtained from the patient.

It is known to acquire physiological signals with sensors attached at apatient's body surface. For example, an electrocardiograph senseselectrical signals that are generated by a patient's heart withelectrodes attached to the patient's chest and limbs. The electrodesproduce one or more electrocardiogram (ECG) signals or leads. Forexample, ten electrodes may be attached to the patient to produce atwelve-lead ECG.

The electrocardiograph has a long history of being an important tool indiagnosing heart disease. While more and more new diagnostic tools areinvented in cardiology (e.g., imaging technology), theelectrocardiograph still remains an indispensable diagnostic tool.However, the presentation of the conventional twelve-lead ECG hasremained relatively the same over the past half century.

SUMMARY OF THE INVENTION

A conventional twelve-lead ECG has limitations due to the limitedinformation it contains. Accordingly, it would be beneficial to acquirea typical twelve-lead ECG (or similar representation of a physiologicalsignal) and supplement the acquired multi-lead ECG with information fromadditional leads obtained using transformations that are derived frompreviously studied patients. With the acquired ECG and the supplementedinformation, a refined representation of the physiological signal may begenerated. The refined representation may be used to provide a moredetailed display of the physiological signal or to generate an optimallead-set for further analysis.

In a first embodiment, the invention provides a method of displaying arepresentation of a physiological signal produced by a patient. Themethod includes the acts of obtaining a portion of at least onephysiological signal acquired from the patient, determining an area todisplay, and constructing a virtual image representing at least aportion of the functional activity of the organ of interest. The virtualimage including (M) polygonal areas. The method further includestransforming the obtained signal to a plurality of values, assigningeach value to one of the (M) polygonal areas, assigning a visualcharacteristic to each polygonal area based in part on the assignedvalues, and displaying at least a portion of the virtual image includingthe assigned visual characteristics.

In a second embodiment, the invention provides a method of analyzing aphysiological signal produced by a patient and generates an optimal setof signals for particular diagnosis. The method includes the acts ofobtaining (N) voltages from a signal (e.g., a multi-lead ECG)representing the physiological signal, converting the (N) voltages to(M) values, where (M) is greater than (N), and optimizing the (M) valuesto (P) values, where (P) is less than (M).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a physiological-signal-analysis deviceembodying the invention.

FIG. 2 is a representation of an electrical signal that is generated bya patient's heart.

FIG. 3 is a matrix representing a plurality of sampled ECG leads.

FIGS. 4A and 4B are schematic representations of front and back bodyportions, respectively, of a patient.

FIG. 5 is a matrix representing a plurality of values at a plurality ofsample points.

FIG. 6 is a vector having a plurality of integrated values.

FIG. 7 is an “Integrated Processed Map” having one hundred ninety-twocells.

FIG. 8 is a matrix including data relating to a plurality of previouslystudied patients.

FIG. 9 is a mean vector of the matrix in FIG. 8.

FIG. 10 is a schematic diagram of a neural network model.

DETAILED DESCRIPTION

Before any embodiments of the invention are explained in full detail, itis to be understood that the invention is not limited in its applicationto the details of construction and the arrangement of components setforth in the following description or illustrated in the followingdrawings. The invention is capable of other embodiments and of beingpracticed or of being carried out in various ways. Also, it is to beunderstood that the phraseology and terminology used herein is for thepurpose of description and should not be regarded as limiting. The useof “including,” “comprising,” or “having” and variations thereof hereinis meant to encompass the items listed thereafter and equivalentsthereof as well as additional items.

A physiological-signal-analysis device 100 embodying the invention isschematically shown in FIG. 1. In general terms, thephysiological-signal-analysis device 100 includes one or morephysiological-signal-input devices 105, a central unit 110, one or moreoperator-input devices 115, and one or more output devices 120. Forexample, the physiological-signal-analysis device 100 may be anelectrocardiograph that acquires electrocardiogram (ECG) signals. It isenvisioned that some aspects of the invention may apply for otherphysiological signals, especially electrical physiological signals(e.g., electromyography signals). For the purposes of simplifying thedetailed description and unless specified otherwise, thephysiological-signal-analysis device 100 is an electrocardiograph thatacquires ECG signals.

The one or more physiological-signal-input devices 105 include aplurality of electrodes E₁, E₂ . . . E_(n) that are connectable to apatient. The electrodes E₁, E₂ . . . E_(n) sense electrical activity(e.g., ECG signals) generated by the patient. Specifically and for theelectrocardiograph, the electrodes sense electrical signals that aregenerated by a patient's heart. The number of electrodes E₁, E₂ . . .E_(n) may vary. But for the embodiment shown, the number of electrodesis equal to ten and are connected to the patient in a standardtwelve-lead ECG configuration.

The electrodes E₁, E₂ . . . E_(n) are connected to the central unit 110by an interface cable 125. The interface cable 125 provides directcommunication between the electrodes E₁, E₂ . . . E_(n) and an inputterminal 130. The interface cable 125 allows for transmission of thesensed ECG signals from the patient to the central unit 110. Theinterface cable 125 is preferably a passive cable but, alternatively,the cable 125 may contain active circuitry for amplifying and combiningthe ECG signals into ECG leads (discussed further below). In otherembodiments, the electrodes E₁, E₂ . . . E_(n) may be in communicationwith the central unit 110 through a telemetry-based transmitter thattransmits radio frequency (“RF”) signals to one or more antennasconnected to the central unit 110.

For other physiological-analysis devices (e.g., a patient monitor), theone or more physiological-signal-input devices 105 may further includeother physiological sensors S₁, S₂ . . . S_(n). The sensors S₁, S₂ . . .S_(n) are connectable to the patient and acquire physiological signalsfrom the patient. For example, the sensors may include noninvasive bloodpressure sensors, carbon dioxide sensors, pulse-oximetry sensors,temperature sensors, etc. Similar to electrodes E₁, E₂ . . . E_(n) andfor the embodiment shown, the one or more sensors S₁, S₂ . . . S_(n) areconnected to the central processing unit 110 at input terminals 135 byinterface cables 140. In other embodiments, the one or more sensors S₁,S₂ . . . S_(n) may be in communication with the central processing unitvia a telemetry transmitter as described above.

The operator-input device 115 allows an operator (e.g., a technician,nurse, doctor, etc.) to control the physiological-signal-analysis device100 and/or to provide data to the central unit 110. The operator-inputdevice 115 may be incorporated within the central unit 110 (e.g., one ormore push buttons, one or more trim knobs, a pointing device, a keyboardetc.) or, alternatively, may be a stand-alone device (e.g., astand-alone keyboard, etc.). Example operator-input devices 115 includea trim knob, a keyboard, a keypad, a touch screen, a pointing device(e.g., a mouse, a trackball), etc. Further and for some aspects of theinvention, the one or more operator-input devices 115 may include datastorage devices that include previously recorded physiological signals.Example data storage devices include magnetic-storage devices (e.g., amagnetic-disc drive), optical-storage devices (e.g., CD, DVD, etc.), andsimilar storage devices.

The central unit 110 includes a power supply 147. The power supply 147powers the physiological-signal-analysis device 100 and receives inputpower either by an external power source 155 or an internal power source160 (e.g., a battery, a solar cell, etc.). The central unit 110 alsoincludes amplifying-and-filtering circuitry 165, analog-to-digital (A/D)conversion circuitry 170, and an analysis module 175.

The amplifying-and-filtering circuitry 165, the A/D conversion circuitry170, and the analysis module 175 may include discrete circuitry,integrated circuitry (e.g., an application-specific-integrated circuit),a processor and memory device combination, or combination of each typeof circuit.

The amplifying-and-filtering circuitry 165 receives the physiologicalsignals from the input terminals 130 and 135, and amplifies and filters(i.e., conditions) the physiological signals. For example, theamplifying-and-filtering circuitry 165 includes an instrumentationamplifier 180. The instrumentation amplifier 180 receives the ECGsignals, amplifies the signals, and filters the signals to create amulti-lead ECG. The number of leads of the multi-lead ECG may varywithout changing the scope of the invention. However, the preferrednumber of ECG leads is equal to twelve leads or sixteen leads.

The A/D conversion circuitry 170 is electrically connected to theinstrumentation amplifier 180. The A/D conversion circuitry 170 receivesthe amplified and filtered physiological signals and converts thesignals into digital physiological signals (e.g., a digital multi-leadECG.) The digital physiological signals are then provided to theanalysis module 175, which is coupled to the A/D conversion circuitry170.

The analysis module 175 reads the digital physiological signals,analyzes the signals to create a developed signal, analyzes thedeveloped signal to create a classification output, and displays thedeveloped signals and/or the resulting classification output to theoperator. In the embodiment shown, the analysis module 175 includes amicroprocessor 182 and internal memory 185. The microprocessor 182interprets and executes instructions stored as one or more softwaremodules in internal memory 185. The memory 185 includes program storagememory 190 for storing the one or more software modules and data storagememory 195 for storing data. The implementation of the softwareincluding the one or more software modules is discussed in furtherdetail below.

The output devices 120 may include a printer, a display, a storagedevice (e.g., a magnetic-disc drive, a read/write CD-ROM, etc.), aserver or other processing unit connected via a network 200. Of course,other output devices may be added or attached (e.g., an audio-outputdevice), and/or one or more output devices may be incorporated withinthe central unit 110. Additionally, not all of the outputs 120 arerequired for operation of the physiological-signal-analysis device 100.

In addition to the physiological-signal-analysis devices 100 describedthus far, one skilled in the art will realize that some aspects of theinvention may not require all elements described above. For example, thephysiological signals may be previously recorded and supplied to thecentral unit via the operator-input device 115 or the network 200. Forthese embodiments, the physiological signals were previously recorded byother physiological-signal-analysis devices and are provided to thephysiological-signal-analysis device 100 for analysis by the softwaremodules described below. For a specific example, thephysiological-signal-analysis device may be a standard personal computerincluding software modules that analyze obtained physiological signalspreviously stored on computer-readable media. Other devices are possibleas is known in the art.

Having described the basic architecture of thephysiological-signal-analysis device 100, different embodiments ofoperation are explained below. As was stated above, for simplifying theexplanation of the detailed description and unless specified otherwise,the physiological-signal-analysis device 100 is an electrocardiographthat acquires a twelve-lead ECG.

In operation, an operator activates the electrocardiograph resulting inthe software initializing the microprocessor 182 as is well known in theart. The operator then attaches the electrodes E₁, E₂ . . . E_(n) to thepatient in a standard twelve-lead configuration and informs theelectrocardiograph, via the data-entry device 115, to acquire thetwelve-lead ECG signals. The electrocardiograph acquires and stores asegment of the multi-lead ECG as is known in the art. The storedmulti-lead ECG includes a plurality of samples or data points for eachlead, and the segment typically includes multiple cycles of thephysiological signal. For example, the electrocardiograph typicallyrecords multiple cardiac cycles of the electrical signals that aregenerated by a patient's heart. However, for some aspects of theinvention or for other physiological-signal-analysis devices 100, thesegment may be a complete cardiac cycle, only a portion of the cardiaccycle, or may be only one sample of the cardiac cycle. For example, ifthe physiological-signal-analysis device is a patient monitor, then theacquired and stored multi-lead ECG signal may be a single sample usedfor displaying a representation of the physiological signal.

Once the electrocardiograph stores the multi-lead ECG signalrepresenting the physiological signal, the electrocardiograph analyzesthe multi-lead ECG signal to create a visual representation of at leasta portion of the physiological signal and/or to create a classificationof at least a portion of the physiological signal. For the descriptionbelow and unless specified otherwise, the electrocardiograph willperform both analyses.

Displaying a Representation of a Physiological Signal

When analyzing the multi-lead ECG signal to create a visualrepresentation of the physiological signal, the software obtains aportion of the raw or processed, such as a representative, cardiac cyclefor analysis. The obtained portion may be for only one sample point ormay be for multiple sample points of the multi-lead ECG. For example,the software may provide a visual representation of the ST-segment 250(FIG. 2) of the cardiac cycle. However, the visual representation may bea representation of the Q-wave, R-wave, T-wave, etc., or a combinationof multiple portions of the cardiac cycle (e.g., ST-segment and T-wavecombination). In some embodiments, the operator may select the portionof the cardiac cycle to analyze. For the description below, the operatorselects the ST-segment 250 for analysis. Upon selecting the ST-segmentfor analysis, the software analyzes ST-segment data from a particularinstant of time. The operator may then request to analyze the ST-segmentdata from other time instants one at a time and “cycle” through thesegments for observing the dynamic changes of ST segment distribution inthe body surface map.

As shown in FIG. 2, the letter (A) represents the beginning of theanalyzed portion and the letter (B) represents the ending of theanalyzed portion. The value of (B) is equal to (A+C), where (C)represents the number of samples in the portion of interest. Forexample, if the stored twelve-lead ECG for the complete cardiac cycleincludes five hundred samples, then the portion of interest (e.g., theST-segment) may be between (A=200) and (B=250), where (C=50). Thevoltages or data for all of the leads may be stored in a matrix (V) asis shown in FIG. 3. Each row of the table includes a lead having sampledvoltages and each column is a sample point. Thus, any particular voltageis represented by (V_(i,j)), where i=1 . . . N and j=A . . . B.

After a portion of the multi-lead ECG is obtained, the operator firstselects a representation of the patient's exterior to display. Forexample, the operator may want to view the front body portion 255 (FIG.4 a), the back body portion 260 (FIG. 4 b), or both the front and backbody portions 255 and 260. Similarly, the operator may want to select asmaller region of the front and back portions 255 and 260. For thedescription below, it is assumed the operator selects both the front andback body portions 255 and 260. If a smaller area is selected, then, forsome embodiments, the one or more acts discussed below are performed forthe selected area only. For other embodiments, the software module doesthe analysis for the largest representative area (e.g. the front andback body portions 255 and 260), but only displays the selected area. Inother embodiments, the software selects the area based on a defaultentry (e.g., no specified area is selected).

After determining an area to display, the software constructs a virtualimage representing the selected portion of the patient's body surface.For the embodiment described, the virtual image is a three-dimensional(3D) surface area representing the front and back body portions 255 and260 of the patient. When constructing the virtual image, the image isequally divided into (M) polygonal areas 262 (FIG. 4 a). In thepreferred embodiment, the value of (M) is equal to 192 (e.g., 81 for thefront body portion 255, 81 for the back body portion 260), eachpolygonal area 262 is a four-sided rectangle, each polygonal area 262has an equal amount of area, and all of the representative area isdivided into the polygonal areas 262. However, for other embodiments,variations of the above requirements may be used. For example, thenumber of polygonal areas 262 may vary, the shape of each polygonal area262 may vary, the size of the polygonal areas 262 may vary, etc.

The software then converts the voltages of each sample point (j) to aplurality of values. For example and for the twelve-lead ECG, thesoftware takes the first sample of each lead (i.e., V_(1,A) . . .V_(N,A)) and converts the (N) voltages to (M) values (i.e., V′_(1,A) . .. V′_(M,A)), where (M) is greater than (N)). For the preferredembodiment, the conversion uses the technique described in Robert Lux,“Mapping techniques”, in Comprehensive Electrocardiology, pp. 1001-1014,by P Macfarlane, Pergamon Press, 1989. Other conversion techniques maybe used as is known in the art. By performing the conversion, thesoftware uses information obtained from previously studied patients tosupplement the acquired multi-lead signal. In other words, based on thepreviously studied patients, the software transforms the (N) multi-leadsignal to a hypothetical (M) “multi-lead” signal. Of course, othertechniques may be used and, depending on the technique used, the numberof leads (N) and number of values (M) may vary. If the selected portionof the cardiac cycle includes multiple data points for each lead (e.g.,the ST-segment is selected), then the software converts the (N) voltagesof each sample to (M) values. The new (M) hypothetical leads may also bestored as a matrix (V′) as is shown in FIG. 5. Each row of the tableincludes a “hypothetical” lead having (C) values and each column is asample point. Thus, any particular value is represented by the letter(V′_(i,j)), where i=1 . . . M and j=A . . . B.

If the selected portion of the cardiac cycle includes multiple datapoints, then the software also calculates the integral for eachhypothetical lead over the A-B segment. For example, the calculation ofthe integral may be made using the formula:${v_{i}^{''} = {\sum\limits_{j = A}^{B}v_{i,j}^{\prime}}};{i = {1\quad\ldots\quad{M.}}}$

Calculating the integral of each value point over the A-B segmentreduces or condenses the columns down to just one column or vector (V″)(FIG. 6) having (M) hypothetical lead values.

After reducing the (C) sample points to one vector (V″), the resultingcolumn creates an “Integrated Potential Map” (IPM). As was explainedabove, the multi-dimensional surface area is divided into (M) polygonalareas. Each integrated value v″_(i) is assigned or “mapped” to one ofthe polygonal areas. For the preferred embodiment, the resulting onehundred ninety-two integral values are assigned to the one hundredninety-two polygonal areas using a one-to-one relationship. Theassigning of the values to a polygonal area results in the IPM having(M) cells. For the specific embodiment described, an IPM 300 (FIG. 7) iscreated having 192 cells where each cell includes a value V_(i)″. Thecells 1-48 and 145-192 are values corresponding to the back of thepatient and cells 49-144 correspond to the front of the patient.

After assigning the values, a visual characteristic is assigned to eachpolygonal area. As used herein, the visual characteristic is adistinguishing visual attribute for each area. For example, depending onthe value of the area, a color is assigned to the area. In other words,if a value is within a first range, then green may be assigned to thatarea. Further, if a value is within a second range, then red may beassigned to that area. A pseudo color-coding scheme is employed.Similarly, other characters or symbols may be used in place of thecolors and other algorithms may be used for determining the visualcharacteristic. Of course, if a selected portion of the display isrequested, then the visual characteristics may be assigned only to therequest portion.

After assigning the visual characteristics, the software displays theselected portion of the multidimensional area. The display may be via avisual display device such as a monitor, or a hard-copy printer.Alternatively, the outcome may be stored in a memory device and recalledlater by the electrocardiograph or by another device 100. Further, thesoftware may highlight the original location of the electrodes, orhighlight the location of the original (N) leads.

When viewing the display, the operator may determine whether the displayis typical or normal, or whether the display is atypical (e.g., thepatient has acute cardiac ischemia or myocardial infarction). An examplesoftware tool that may be used to help generate the display is an OpenGLbrand software package sold included in most operating systems.

Although, the above description was over a portion of the cardiac cyclehaving multiple samples, a body potential map may be created for justone sample. The software performs the same steps as above except, sinceonly one row of samples is transformed, the software does not performthe integration to create a vector (V″).

Classifying a Physiological Signal

In addition to displaying a representation of the physiological signal,the physiological-signal-analysis device 100 may also analyze andclassify the physiological signal. In some embodiments of the invention,not all of the above-identified acts are performed. Further, in otherembodiments of the invention, the physiological-signal-analysis device100 only performs the function of displaying the representation.Preferably, the physiological-signal-analysis device 100 performs boththe above acts and classifies the physiological signal.

As was discussed above, the portion of interest used for thisdescription is the ST-segment 250. However it is envisioned that theanalysis may be extended to other components or portions of the cardiaccycle. For example, the Q-wave, R-wave, ST-segment, T-wave, or acombination of the different components (e.g., ST-segment and T-wavecombination) may be analyzed. When analyzing the combination ofdifferent components, each component is analyzed separately, and thenthe resulting separate analyses are analyzed together. In addition, thesoftware may combine the separately analyzed portions to analyze thecomplete or whole cardiac cycle. Further, the software may separatelyanalyze multiple cardiac cycles and average the resulting analyzedcycles.

With the IPM 300, the software reduces the (M) hypothetical leads to (P)optimal values or leads for a particular diagnosis, for example, acutecardiac ischemia. Reducing the number of values from (M) to (P) providesbetter analysis over the original lead-set. In other words, the original(N) leads are expanded to (M) hypothetical lead values based on priorobtained patient information, and then reduced to (P) optional leadvalues containing the optimal amount of information. The (P) optionallead values may then be applied to pattern recognition modules toclassify the portion of interest. By optimizing the IPM 300, ahypothetical or optimal lead set is created providing more informationfrom the hypothetical lead set than the original lead set. In thepreferred embodiment, the software reduces the one hundred ninety-twointegral amplitude values down to twelve optimal lead values. However,the number of optimal values may vary.

For the preferred embodiment, the software uses principal componentanalysis (PCA) to condense the (M) hypothetical lead values to the (P)optimal lead values based on a previously created database of patients.The purpose of PCA is to remove redundant information and obtain theoptimal feature set for classification or further analysis. The softwareobtains from memory, a previous stored database having a substantialnumber of entries (e.g., six thousand patients). The letter (D)represents the number of patients. The entries include (M) values ofpreviously studied patients having various conditions. For example, ifthe ST-segment is the portion of interest then the database may includetwo thousand samples of patients diagnosed with acute myocardialinfarction, two thousand patients diagnosed with unstable angina, andtwo thousand patients diagnosed as being normal. The data is stored as amatrix (X) (FIG. 8), which is a (M) by (D) matrix. In other words, eachcolumn represents a patient having an IPM. The value of (D) is onlylimited to the amount of memory available, and other types of patientswith different conditions or information may be used.

After obtaining the stored matrix, the software computes a covariancematrix (C_(x) ) with the formulaC _(X)=(X−Y _(X))(X−Y _(X))^(T)where (X) is matrix 305, (Y) is the mean vector of X, and (X−Y)^(T) isthe transpose of (X−Y). The mean vector Y is represented in FIG. 9 andeach value y_(i) is calculated using the formula:${y_{i} = {\frac{1}{D}{\sum\limits_{j = 1}^{D}x_{i,j}}}},{i = {1\quad\ldots\quad M}}$

The covariance matrix (C_(x)) has a size (M) by (M) (e.g., one hundredninety-two by one hundred ninety-two).

After calculating the covariance matrix (C_(x)), the software appliesPCA analysis to the covariance matrix (C_(x)) with the formulaC _(X) =USV ^(T),where (U) and (V) are orthogonal matrices, S is a diagonal matrix (alsoreferred to as principal components). The diagonal elements of thediagonal matrix are arranged in descending order. The matrices (U), (S)and (V) each have a size (M) by (M). The matrices (U) and (V) are eachorthogonal; that is, their columns are orthogonal, i.e.,${{\sum\limits_{i = 1}^{M}{U_{i,k}U_{i,n}}} = {{\delta_{kn}\quad 1} \leq k \leq M}},{1 \leq n \leq M}$${{\sum\limits_{j = 1}^{M}{V_{j,k}V_{j,n}}} = {{\delta_{kn}\quad 1} \leq k \leq M}},{1 \leq n \leq M}$

For the embodiment described, the first twelve elements represent morethan 98% of the signals energy, i.e.${\frac{\sum\limits_{i = 1}^{12}s_{i}}{\sum\limits_{j = 1}^{192}s_{j}}*100} > 98.$By retaining the first twelve principal components, a majority of theinformation is kept.

The software uses the first (P) columns (e.g., P=12) of matrix (U) as(U₁), uses the first (P) by (P) submatrix from the upper comer of (S) as(S₁), and uses the first (P) columns from (V) as (V₁). The approximationmatrix, {tilde over (C)}_(x), may then be formed as follows:{tilde over (C)} _(x) =U ₁ S ₁ V ₁ ^(T)

After calculating the submatrix ({tilde over (C)}_(X)), the softwarecalculates the optimized values or leads. U₁ is used for representingprojection matrix. That is, the vector Y_((M)input) (e.g., vector V″) isreduced or condensed to a new (P) lead vector with the equation:Y _((P)new) =U ¹ ^(T) Y _(M)where Y(P)new is the new (P) optimal lead set based on PCA reduction andthe input lead set. For the embodiment shown, Y_((M)input) is vector V″(FIG. 6). However, for other embodiments, other input lead sets may beused.

With the new optimal set, the software can classify the segment. Forexample, the software may locate the ST elevation and maximum STdepression for the new optimal lead set.

The optimal lead set may also be used for further interpretation. Aninterpretation/pattern recognition method may be used to differentiateoptimal lead sets into normal and abnormal categories. Patternrecognition models may include neural networks, fuzzy logic, or otherstatistical models like a Bayesian classifier. An example of a neuralnetwork model is shown in the following FIG. 10, where the model has 3layers: input layer 400, hidden layer 405 and output iayer 410. Theinput layer receives the (P) values for the optimal lead set, the hiddenlayer applies nonlinear mapping for the optimal lead set, and the outputlayer generates prediction results based on the nonlinear mapping. Otherpattern recognition models may be used as is known in the art.

As can be seen from the above, the invention provides a new and usefulphysiological-signal-analysis device for displaying and analyzing aphysiological signal of a patient. The invention also provides a new anduseful method of displaying and analyzing a physiological signal of apatient, and a new and useful software tool for displaying and analyzinga physiological signal. Various features and advantages of the inventionare set forth in the following claims.

1. A method of displaying a representation of a physiological signalproduced by an organ of interest of the patient, the method comprisingthe acts of: obtaining a portion of at least one physiological signal,the obtaining act including acquiring the at least one physiologicalsignal from the exterior of the patient; determining an area to display;constructing a virtual image including (M) polygonal areas; transformingthe obtained signal to a plurality of values; assigning each value toone of the (M) polygonal areas; assigning a visual characteristic toeach polygonal area based in part on the assigned values; and displayingat least a portion of the virtual image including the assigned visualcharacteristics.
 2. A method as set forth in claim 1 wherein the act ofobtaining a portion of at least one physiological signal includes theacts of placing a plurality of electrodes on the exterior of the patientand obtaining at least a portion of a multi-lead electrical signalacquired from the plurality of electrodes.
 3. A method as set forth inclaim 1 wherein the act of obtaining a portion of at least onephysiological signal includes the act of obtaining at least a portion ofa multi-lead electrocardiogram (ECG) acquired from the patient'sexterior.
 4. A method as set forth in claim 3 wherein the multi-lead ECGis a twelve lead ECG.
 5. A method as set forth in claim 4 wherein theobtained portion of the representative signal includes one data pointfor each lead.
 6. A method as set forth in claim 4 wherein the obtainedportion of the multi-lead ECG includes a plurality of data pointsrepresenting a portion of the cardiac cycle.
 7. A method as set forth inclaim 6 wherein the obtained portion of the multi-lead ECG includes theST-wave of the cardiac cycle.
 8. A method as set forth in claim 1wherein the act of obtaining at least a portion of the at least onephysiological signal includes the acts of attaching a sensor to theexterior of the patient and sensing the physiological signal with thesensor to obtain the at least one physiological signal.
 9. A method asset forth in claim 8 wherein the sensor includes a plurality ofelectrodes.
 10. A method of displaying a representation of aphysiological signal produced by an organ of interest of the patient,the method comprising the acts of: obtaining a portion of at least onephysiological signal acquired from the exterior of the patient,including the act of reading the at least one physiological signal froma memory device; determining an area to display; constructing a virtualimage including (M) polygonal areas; transforming the obtained signal toa plurality of values; assigning each value to one of the (M) polygonalareas; assigning a visual characteristic to each polygonal area based inpart on the assigned values; and displaying at least a portion of thevirtual image including the assigned visual characteristics.
 11. Amethod as set forth in claim 1 wherein the virtual image represents atleast a portion of the body surface of the patient.
 12. A method as setforth in claim 1 wherein each polygonal area has a size and a shape, andwherein the sizes and shapes are equivalent areas.
 13. A method as setforth in claim 1 wherein each polygonal area is a four-sided polygon.14. A method as set forth in claim 1 wherein the act of constructing avirtual image includes the acts of determining the portion of thepatient to be represented, creating a multidimensional imagerepresenting the portion of the patient to be represented, determiningthe value of (M), and dividing the representative surface area into (M)polygonal areas.
 15. A method as set forth in claim 1 wherein the act ofassigning a visual characteristic to each polygonal area includesassigning a color to each polygonal area, based at least in part on thecorresponding assigned value.
 16. A method as set forth in claim 1wherein the act of assigning a visual characteristic to each polygonalarea includes assigning a character to each polygonal area, based atleast in part on the corresponding assigned value.
 17. A method as setforth in claim 5 wherein the act of transforming the obtained signalsincludes transforming the data points of the twelve leads to (M) values,and wherein the act of assigning each value to one of the (M) polygonalareas result in each polygonal area having one of the (M) values.
 18. Amethod as set forth in claim 1 wherein (M) is equal to one hundredninety-two.
 19. A method of displaying a representation of anelectrocardiogram (ECG), the method comprising the acts of: obtaining atleast a portion of a multi-lead ECG acquired from the patient'sexterior; determining an area to display; constructing a virtual imageincluding (M) polygonal areas; transforming the obtain portion of themulti-lead ECG to (M) values; assigning each value to one of the (M)polygonal areas, the assigning act resulting in each polygonal areahaving one of the (M) values; assigning a visual characteristic to eachpolygonal area based in part on the assigned values; and displaying atleast a portion of the virtual image including the assigned visualcharacteristics.
 20. A method as set forth in claim 19 wherein thenumber of obtained leads is twelve leads.
 21. A method as set forth inclaim 20 wherein (M) is equal to one hundred ninety-two.
 22. A method asset forth in claim 19 wherein the obtained portion of the ECG includeone data point for each lead.
 23. A method as set forth in claim 19wherein the obtained portion of the ECG includes a plurality of datapoints for each lead representing a portion of the cardiac cycle.
 24. Amethod as set forth in claim 19 wherein the act of obtaining at least aportion of a multi-lead ECG includes the acts of attaching a sensor tothe patient's exterior, sensing the ECG with the sensor, and creatingthe multi-lead ECG.
 25. A method as set forth in claim 24 wherein thesensor includes a plurality of electrodes.
 26. A method as set forth inclaim 19 wherein the act of obtaining at least a portion of a multi-leadECG includes the acts of reading at least a portion of the multi-leadECG from a memory device.
 27. A method as set forth in claim 19 whereinthe virtual image is a three-dimensional surface area representing atleast a portion of the patient.
 28. A method as set forth in claim 19wherein the (M) polygonal areas are regions on the three-dimensionalsurface area, wherein the (M) polygonal areas do not overlap, andwherein each polygonal area includes the same amount of area.
 29. Amethod as set forth in claim 28 wherein each polygonal area is afour-sided polygon.
 30. A method as set forth in claim 19 wherein theact of constructing a virtual image includes the acts of determining theportion of the patient to be represented, creating a multidimensionalsurface area representing the portion of the patient, determining thevalue of (M), and dividing the representative surface area into (M)polygonal areas.
 31. A method as set forth in claim 19 wherein the actof assigning a visual characteristic to each polygonal area includesassigning a color to each polygonal area based at least in part on thecorresponding assigned value.
 32. A method of analyzing a physiologicalsignal produced by a patient and generating an optimal set of signalsfor particular diagnosis, the method comprising the acts of: obtaining(N) voltages from (N) signals, respectively, the (N) signalsrepresenting the physiological signal, (N) being greater than one;converting the (N) voltages to (M) values, where (M) is greater than(N); optimizing the (M) values to (P) values, where (P) is less than(M).
 33. A method as set forth in claim 32 and further comprisingclassifying the physiological signal with the (P) optimized values. 34.A method as set forth in claim 33 wherein the act of classifying thephysiological signal includes the act of applying the (P) optimizedvalues to a pattern recognition model for obtaining a classificationoutput.
 35. A method as set forth in claim 33 wherein the act ofclassifying the physiological signal includes the act of applying the(P) optimized values to a neural network for obtaining a classificationoutput.
 36. A method as set forth in claim 33 wherein the act ofclassifying the physiological signal includes the act of applying the(P) optimized values to a fuzzy algorithm for obtaining a classificationoutput.
 37. A method as set forth in claim 33 wherein the act ofclassifying the physiological signal includes the act of applying the(P) optimized values to a Bayesian decision logic for obtaining aclassification output.
 38. A method as set forth in claim 32 wherein the(N) signals representing the physiological signal form a (N) multi-leadelectrocardiogram.
 39. A method as set forth in claim 38 wherein (N) isequal to twelve, (M) is equal to one hundred ninety-two, and (P) isequal to twelve.
 40. A method as set forth in claim 32 wherein, prior tothe act of optimizing the (M) values to (P) values, the method furthercomprises: repeating the act of obtaining (N) voltages (C) times, therepeating act resulting in (C) sets of (N) voltages; repeating the actof converting the (N) voltages to (M) values for each set of (N)voltages, the repeating act resulting in (C) sets of (M) values; andcondensing the (C) sets of (M) values to one set of (M) values.
 41. Amethod as set forth in claim 40 wherein the physiological signalincludes electrical signals that are generated by the patient's heart ina cardiac cycle, and wherein the (C) sets of (N) voltages are an (N)multi-lead representation of a portion of the cardiac cycle.
 42. Amethod as set forth in claim 32 wherein the act of obtaining the (N)voltages includes the acts of attaching a sensor to the patient'sexterior, sensing the physiological signal with the sensor to obtain (N)analog physiological signals, and sampling each signal to produce the(N) voltages.
 43. A method as set forth in claim 42 wherein the sensorincludes a plurality of electrodes.
 44. A method as set forth in claim32 wherein the act of obtaining (N) voltages includes the act of readingthe (N) voltages from a memory device.
 45. A method as set forth inclaim 40 wherein the (C) sets of (M) values result in (M) virtualsignals having (C) data points, and wherein the act of condensing the(C) sets of (M) values includes the act of integrating the (M) valuesover the (C) data points.
 46. A method as set forth in claim 32 whereinthe act of optimizing the (M) values to (P) values includes the acts ofobtaining a database of previously recorded comparison values, computinga covariance matrix, and applying principal component analysis to thecovariance matrix.
 47. A method as set forth in claim 32 wherein the actof obtaining (N) voltages includes concurrently obtaining the (N)voltages from (N) signals, respectively.
 48. A method as set forth inclaim 40 wherein (N) is equal to twelve, (M) is equal to one hundredninety-two, and (P) is equal to twelve.